Again following the vocabulary used in Bau et al [1]}, we refer to feature maps as the output of every layer in the network–the aggregate of the feature maps makes up the network's learned representation.
We refer to a neuron as a specific channel of the feature map. In this paper, we use the term activation gamut to refer to all the possible values that a particular neuron can produce. We can approximate the activation gamut as the set of a neuron's activation values for each image in a dataset.
